PRODUCTION AND OPERATIONS MANAGEMENT
Year:2019 | Volume:10 | Issue:1 (18) |Papers Count:10

In undesirable facility location problem contrary to desirable location, facilities are located far from service receiver facilities as much as possible. The problem of locating such facilities is discussed in this study. This research is focused on the ‘ not in my backyard’ (NIMBY) phrase which refers to the social phenomena in which residents are opposed to locate undesirable facilities around their houses. Examples of such facilities include electric transmission lines and recycling centers. Due to the opposition typically encountered in constructing an undesirable facility, the facility planner should understand the nature of the NIMBY phenomena and consider it as a key factor in determining facility location. Because of the adverse effects of these facilities, coupled with the uncertainty in the real world, it is estimated taking into account the potential uncertainty. This problem has been considered in the discrete space. The mathematical model is presented and methods to deal with uncertainty and stochastic programing problems modeling and methods used in the study are presented. According to the NP-hard problem, Simulated Annealing suggested for solving problems in large-scale. Numerical experiments to evaluate and validate the mathematical modeling and proposed algorithm are considered and operation of the proposed algorithm to solve various problems with genetic algorithms available in the literature about the problem is compared.

Organizations should consider process improvement for promoting their performance. The main problem is the high volume of process dataset along with the wide variety of their features that leads to increase the complexity in process improvement methods. Previous methods cannot improve processes in a high volume of process dataset. Data mining approach can support improvement methods by identifying valuable hidden patterns in high volume of process dataset. In this paper a framework of using data mining techniques for extracting valuable hidden patterns in high volume of process dataset is developed for presenting the improvement suggestions. In order to evaluate the proposed framework, a real set of processes along with their features were gathered. After that, using classification, clustering, and features selection algorithms, valuable patterns in high volume of process dataset were identified. After evaluating these patterns, improvement suggestions were recommended by the identified patterns. The results showed that identified patterns can support processes improvement activities with recommending the improvement suggestions.

Supply Chain Management (SCM) is a suitable tool to improve economic, social and environmental performance. SCM assessment is an important task for all types of organizations. The DEA method has been widely used to evaluate SCM. By attention supply chain as network data envelopment analysis (DEA) can calculate the efficiency of supply chain with multiple stages. This study examines the efficiency and returns to scale (RTS) of supply chain management of resin manufacturing companies based on network DEA models. We determine returns to scale of resin manufacturing companies as a two-stage process, with crisp and fuzzy data. Fuzzy DEA model is based on 𝛼 approach to measure the efficiency and RTS of supply chain. The proposed models are used to evaluate the efficiency and RTS of supply chain of 27 resin production companies. The six companies were network efficient in the investigation with crisp data, while there are three network efficient companies with fuzzy data.

The success or failure of any organization is directly linked to the quality of its human resources selection (recruitment, measurement, and selection). By reviewing the data of a knowledge job, this paper aims to help improve the selection process of that job. Consequently, the selection of appropriate employees’ rate will increase, and the rate of human resource turnover will decrease. The approach of this paper is Applied Research and the strategy is Case Study. This paper combines two computational techniques (DEA and CART). Data Envelopment Analysis (DEA) is a non-parametric technique that determines the efficiency of individuals, but it does not provide information on the details of factors affecting performance (especially non-numerical factors). In the present study, this deficiency has been resolved using the Classification and Regression Tree (CART) (as a data mining technique). The result of this study has provided a framework for combining DEA and CART in order to discover rules on the recruitment of knowledge workers in a specific job (a knowledge job) and in a specific organization (HFJ Institute). The results indicate that ‘ work experience’ , ‘ average score in the last degree’ and ‘ age’ are related to the employee performance, and therefore it is necessary to be considered in the process of future recruitment of that job.

The purpose of this research is to determine the optimal wholesale and final prices of printed and electronic books in a dual channel supply chain. Here, a comprehensive sensitivity analysis has been conducted on different supply chain parameters such as demand elasticity coefficients, customer tendency to printed books and book production costs. The effect of these variables on optimal prices, demands and final profit is analyzed. Results of centralized and fixed demand mode reveal that if the coefficient of customers' tendency to printed book is bigger than 0. 5, then the final printed books' price will be higher than e-book prices. Pricing in decentralized model is done using the Stackelberg equilibrium. Results show that wholesale prices of p-book in decentralized and fixed demand system are equal to selling prices in centralized system, but the final selling price of p-book in decentralized system is higher than centralized one. In both systems, the e-book selling price is the same. In the fixed demand condition, either e-books are not published or they are simultaneously published with their printed versions; however, in the decreasing demand mode, e-books may be introduced to market at any time during books' life period.

This paper aimed at identifying and analyzing supply chain risks of Saipa automotive company to determine those seemly critical and the appropriate decision for each category to be made. To this end, first, according to company’ s documents and interviews with experts, and using theme analysis method, the identification and categorization of supply chain risks are addressed. In the second step, by using of SNA approach, the most important risks in terms of the effects they have on emerging other risks in the risks’ relationship network are determined. The results are analyzed using the IPM matrix and the necessary decisions are made according to this matrix. According to the results, 48% of the total risks are categorized in financialeconomic, suppliers, information, and transportation categories. Therefore, it seems that paying particular attention to these areas can result in significant improvement in system’ s status.

Pharmaceutical Industry in Iran has difficulties such as inappropriate distribution, expired drugs and late drug delivery to patients. To overcome these issues and reach a sustainable development, the environmental and social aspects must be considered beside the economic aspects. In this paper, we address the Pharmaceutical distribution network and design a novel model that helps the distributing companies to decide strategic (determining the main and local distribution centers) and tactical decision (determining the flow) according to the three aspects of sustainability. The solution through the evolutionary algorithms presents the best selection of local distribution centers and the optimum amount of flow of medicine through the network with respect to three aspects of sustainability.

In the past, traditional channel or retailer was used for selling products but with the development of ecommerce, a large company in the world considers another sale channel like websites. Considering the existence of two channels for sale, choosing the right strategy for pricing has become important. In pricing and production planning, risk is a very important factor. In this paper, outsourcing policies have been used to deal with risks and a new mathematical model is presented for simultaneous decision-making on pricing and outsourcing in a threelevel and two-channel supply chain despite uncertainty. In this paper, a nonlinear model is presented for supply chain profit function. According to the complexity of profit function, a meta-heuristic algorithm based on simulated annealing and scenario-based stochastic model are used to solve the proposed model. The initial parameters of this algorithm are set by Taguchi method. The computational results and sensitivity analysis indicate the effectiveness of the proposed solving method for problem solving.

Iran is a country where the rate of natural disasters is high comparing with other countries. Therefore, humanitarian supply chain management before, during and after disasters is of great importance. Performance assess humanitarian supply chain is one of the important activities in humanitarian supply chain management which specifies the performance and how to improve it. At the beginning, performance dimensions and indicators of the earthquake supply chain in the country were identified. For this purpose, the output of the master's thesis in Tehran University was used and weights of performance dimensions and indicators by normalization factor loadings were obtained. Then, the selected earthquakes were assessed by gray approach. Two scenarios were used to assess reasonably and appropriately. In the first scenario, Just earthquake information was used, but in the second scenario, after setting the standard in qualitative and quantitative indicators, this information is entered in the calculations in the form of a hypothetical earthquake. Results are based on the second scenario which is more complete than the first one. They are: humanitarian supply chain performances in Dashti and Ahar earthquakes were equal and were better than that in Bashagard.

The purpose of this article is to present an approach based on simulation optimization for improving the performance of cellular manufacturing systems through optimizing operator allocation and job dispatching rules in each cell. In this study, we have considered stochastic parameters, machines’ breakdown and multiple products in order to consider the problem as close as possible to real-world situation. The presented approach is composed of Taguchi design of experiments, discrete event simulation, artificial neural networks, and data envelopment analysis. First, controllable and response variables are determined based on the objective of the study and expert judgment. Then, the design of experiments is used in order to develop experimental scenarios base on controllable variables. Furthermore, simulation is used to evaluate experimental scenarios and their related response variables. Then, in order to expand the experimental results to the whole feasible solution space, artificial neural networks is used. Finally, the optimum scenario is determined using data envelopment analysis. After determining the optimum scenario, it is compared to the present condition of the case and the improvements are determined. In order to evaluate the performance of the presented approach, a lathing factory which uses a cellular manufacturing system is considered as the case study.